Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems

By Peihao Dong, Hua Zhang, Geoffrey Y. Li, Ivan Simoes Gaspar, Navid Naderializadeh. IEEE Journal on Selected Topics in Signal Processing, 2019

391 citations recorded by the source corpus.

For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is usually used to reduce the complexity and cost, which poses a very challenging issue in channel estimation. In this paper, deep convolutional neural network (CNN) is employed to address this problem. We first propose a spatial-frequency CNN (SF-CNN) based channel estimation exploiting both the spatial and frequency correlation, where the corrupted channel matrices at adjacent subcarriers are input into the CNN simultaneously. Then, exploiting the temporal correlation in time-varying channels, a spatial-frequency-temporal CNN (SFT-CNN) based approach is developed to further improve the accuracy. Moreover, we design a spatial pilot-reduced CNN (SPR-CNN) to save spatial pilot overhead for channel estimation, where channels in several successive coherence intervals are grouped and estimated by a channel estimation unit with memory. Numerical results show that the proposed SF-CNN and SFT-CNN based approaches outperform the non-ideal minimum mean-squared error (MMSE) estimator but with reduced complexity, and achieve the performance close to the ideal MMSE estimator that is very difficult to be implemented in practical situations. They are also robust to different propagation scenarios. The SPR-CNN based approach achieves comparable performance to SF-CNN and SFT-CNN based approaches while only requires about one-third of spatial pilot overhead at the cost of complexity. The results in this paper clearly demonstrate that deep CNN can efficiently exploit channel correlation to improve the estimation performance for mmWave massive MIMO systems.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b92fef98902f0a1bf0c23e213098a02c378b773e

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In Aerospace Engineering

  1. H. Vincent Poor
  2. Dusist Niyato
  3. Shuguang Cui
  4. Bo Ai
  5. Merouane Debbah
  6. Khaled Ben Letaief
  7. Jiayi Zhang
  8. Zhe Wang

In Electrical and Electronic Engineering

  1. H. Vincent Poor
  2. Merouane Debbah
  3. Khaled Ben Letaief
  4. Shuguang Cui
  5. Dusist Niyato
  6. Jiayi Zhang
  7. Rui Zhang
  8. Bo Ai

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